Is the association causal, or are there alternative explanations?

1. Is the association causal, or are there alternative explanations? Epidemiology matters: a new introduction to methodological foundations Chapter 8

2. Seven steps • Define the population of interest • Conceptualize and create measures of exposures and health indicators • Take a sample of the population • Estimate measures of association between exposures and health indicators of interest • Rigorously evaluate whether the association observed suggests a causal association • Assess the evidence for causes working together • Assess the extent to which the result matters, is externally valid, to other populations Epidemiology Matters – Chapter 1

3. Inferential thinking, chapter 7 In Chapter 7 we asked a conceptual (counterfactual) question: Would the disease have occurred when and how it did without the exposure, or without the amount of exposure that occurred, the timing of exposure, or within the context of multiple exposures? Epidemiology Matters – Chapter 8

4. Inferential thinking, chapter 8 In Chapter 8 we ask a pragmatic question: Does the association that we measure in our data reflect the amount of excess disease that occurred dueto the effects of the exposure, or could there be alternative explanations for the study findings other than a causal explanation? Epidemiology Matters – Chapter 8

5. Exposure causes disease • Complicating causes • Causal thinking in populations • Epidemiologic studies and assessing causes • Summary Epidemiology Matters – Chapter 8

6. Exposure causes disease • Complicating causes • Causal thinking in populations • Epidemiologic studies and assessing causes • Summary Epidemiology Matters – Chapter 8

7. When does exposure cause disease? A counterfactual test to see if an exposure is a cause would require us to: • Take the same person observed over the same time period, once with the exposure and once without the exposure • Hold all other characteristics of the person, place and time constant • Change only the exposure and observe then if the health indicator changes This is, of course, impossible Epidemiology Matters – Chapter 8

8. Non-diseased Diseased Non-exposed Exposed Epidemiology Matters – Chapter 8

9. Observing individualsunder simultaneous conditions Epidemiology Matters – Chapter 8

10. Observing individualsunder simultaneous conditions Person 1: exposure causal Person 2: exposure not causal Epidemiology Matters – Chapter 8

11. Exposure causes disease • Complicating causes • Causal thinking in populations • Epidemiologic studies and assessing causes • Summary Epidemiology Matters – Chapter 8

12. Why would an exposure be causal for Person 1 but not causal for Person 2? Epidemiology Matters – Chapter 8

13. Complicating causes • Many sufficient cause sets can produce particular health indicators • The exposure of interest may be part of only one particular sufficient cause set; there are other sufficient causes that also produce the health indicator of interest Epidemiology Matters – Chapter 8

14. Complicating causes, an example Disease X has two sufficient causes • A, B, and C • E, F, and G Individual exposed to A, B, C, F, and G • Will get the disease • Completes sufficient cause 1 (A, B, and C) Now exposed to E • Completes sufficient cause 2 (E, F, and G) Exposure to E is not causal for this individual because she would have gotten the disease regardless given exposure to A, B, and C Therefore if E is exposure of interest we need to consider A, B, and C as other causes of disease How can we visualize individuals with component causes not included in sufficient causal structure of E? Epidemiology Matters – Chapter 8

15. Component causes of sufficient cause A,B,C - without E Previous example Exposure of interest E Epidemiology Matters – Chapter 8

16. Component causes of sufficient cause A,B,C - without E Previous example Exposure of interest E Person 2 gets disease regardless of exposure E These additional causes complicate causal inference Epidemiology Matters – Chapter 8

17. Exposure causes disease • Complicating causes • Causal thinking in populations • Epidemiologic studies and assessing causes • Summary Epidemiology Matters – Chapter 8

18. Causal thinking in populations • Remember that epidemiological studies investigate groups of people • Therefore, our causal thinking applies to groups of individuals with multiple sufficient causes • We are interested in understanding the number of excess cases of disease that can be removed if we remove a particular cause Epidemiology Matters – Chapter 8

19. Group comparison, example Epidemiology Matters – Chapter 8

20. Group comparison, example Epidemiology Matters – Chapter 8

21. Group comparison, example Excess cases of disease due to causal effect of the exposure on the outcome Epidemiology Matters – Chapter 8

22. Causal association? • Exposure causes disease • Complicating causes • Causal thinking in populations • Epidemiologic studies and assessing causes • Summary Epidemiology Matters – Chapter 8

23. Epidemiologic study design • It is impossible to observe the same people over the same period with and without exposure • Instead we use group comparison of exposed and unexposed groups, often observed in parallel over a similar time period • Ideally we want the unexposed group in an epidemiologic study to represent the experience of exposed grouphad they not been exposed • However, what can complicate this approach is if there are imbalances in the comparability of these groups allowing there to be different causes in each group • It is therefore essential to know how comparablethese groups are to each other, i.e., how close is the unexposed group to what we would expect the exposed group to resemble if they were not exposed? Epidemiology Matters – Chapter 8

24. Distribution of additional causes To assess comparability we need to know about the distribution of other causes of disease between exposed and unexposed groups Epidemiology Matters – Chapter 8

25. Comparing groups Epidemiologic study #1 Epidemiology Matters – Chapter 8

26. Comparing groups Epidemiologic study #1 Epidemiologic study #2 Epidemiology Matters – Chapter 8

27. Comparing groups Epidemiologic study #1 Epidemiologic study #2 Even distribution of dots across exposure conditions Exposure conditions are comparable Epidemiology Matters – Chapter 8

28. Comparing groups Epidemiologic study #1 Epidemiologic study #2 Uneven distribution of dots across exposure conditions These exposure conditions are not comparable Even distribution of dots across exposure conditions Exposure conditions are comparable Epidemiology Matters – Chapter 8

29. Causal association? • Exposure causes disease • Complicating causes • Causal thinking in populations • Epidemiologic studies and assessing causes • Summary Epidemiology Matters – Chapter 8

30. Non-comparability • To replicate a counterfactual paradigm we want to observe the same group at same time with the only variable changing being exposure • This is infeasible. Instead we compare groups of people and aim to keep the distribution of all other variables equal between the groups • Failure to achieve this results in group ‘non-comparability’ Epidemiology Matters – Chapter 8

31. Seven steps • Define the population of interest • Conceptualize and create measures of exposures and health indicators • Take a sample of the population • Estimate measures of association between exposures and health indicators of interest • Rigorously evaluate whether the association observed suggests a causal association • Assess the evidence for causes working together • Assess the extent to which the result matters, is externally valid, to other populations Epidemiology Matters – Chapter 1

32. epidemiologymatters.org Epidemiology Matters – Chapter 1